| Abstract: |
The increasing demand for renewable energy sources has intensified the focus on optimizing the performance of solar photovoltaic (PV) systems. A critical component for maximizing energy extraction is the Maximum Power Point Tracking (MPPT) system, which continuously adjusts the operating point of the PV array to harvest maximum power under prevailing conditions. This paper presents a comprehensive review of Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT strategies, a class of intelligent control algorithms that have demonstrated significant potential in enhancing PV efficiency. The review synthesizes findings from recent research, highlighting the evolution from conventional MPPT techniques like Perturb and Observe (P&O) and Incremental Conductance (IncCond) to more advanced AI-driven approaches. ANFIS, by integrating the learning capabilities of neural networks with the rule-based reasoning of fuzzy logic, offers superior adaptability to dynamic environmental conditions such as varying solar irradiance and temperature, and is particularly effective in mitigating issues like partial shading. Studies consistently report high tracking efficiencies, often exceeding 99%, for ANFIS-based MPPT controllers, with advantages including faster convergence times, reduced steady-state oscillations, and improved global maximum power point (GMPP) detection compared to traditional methods. This review critically analyzes various ANFIS implementations, discussing their architectures, training methodologies, and performance metrics. It also explores hybrid approaches combining ANFIS with metaheuristic algorithms and advanced control strategies, and identifies avenues for future research to further refine these advanced MPPT strategies for robust and efficient solar energy harvesting in real-world applications. |
